/*
 * Javlov - a Java toolkit for reinforcement learning with multi-agent support.
 * 
 * Copyright (c) 2009 Matthijs Snel
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package net.javlov;

/**
 * Agent that just returns actions according to its policy, no learning.
 * 
 * @author Matthijs Snel
 *
 */
public class SimpleAgent implements Agent {

	protected Policy policy;
	
	protected Option currentOption;
	
	protected boolean interruptOptions = false;
	
	@Override
	public <T> Action doStep( State<T> s, double reward ) {
		//let option learn and pick an action, and determine if it is finished
		Action a = currentOption.doStep(s, reward);
		
		//if current option has finished, or agent decides to interrupt the current
		//option, pick a different option and return that option's first action.
		if ( currentOption.isFinished() || interruptCurrentOption(s) ) {
			currentOptionHasFinished();
			currentOption = policy.getOption(s);
			return currentOption.firstStep(s);
		}
		
		//otherwise, continue executing the policy of current option
		return a;
	}

	@Override
	public <T> Action firstStep( State<T> s ) {
		currentOption = policy.getOption(s);
		return currentOption.firstStep(s);
	}
	
	@Override
	public void init() {
		currentOption = null;
		policy.init();
	}
	
	@Override
	public void reset() {
		currentOption = null;
		policy.reset();
	}
	
	public Policy getPolicy() {
		return policy;
	}

	public void setPolicy(Policy p) {
		policy = p;
	}
	
	@Override
	public LearningRate getLearnRate() {
		return null;
	}

	@Override
	public void setLearnRate(LearningRate rate) {
		throw new UnsupportedOperationException(getClass() + " is not a learning agent.");
	}
	
	public boolean interruptsOptions() {
		return interruptOptions;
	}

	public void setInterruptOptions(boolean interruptOptions) {
		this.interruptOptions = interruptOptions;
	}
	
	/**
	 * This method is here so thay any subclasses know a different option
	 * has been picked.
	 */
	protected void currentOptionHasFinished() {}
	
	protected <T> boolean interruptCurrentOption(State<T> s) {
		if ( interruptOptions )
			currentOption.setFinished();
		return interruptOptions;
	}

	@Override
	public <T> void lastStep(State<T> s, double reward) {
		//do nothing; no learning
	}

}

